N - mixture models by species

modelling N by site to get relative abundance

abundance by site will be used as a cov on predator occupancy

7 models evaluated, dot, jdt + jdtSQ, lure + jdt + jdtSQ


Species:      GoldenMantledGroundSquirrel



Metadata Summary:

N_sites N_counts N_detections rep_period iterations burnin thin
127 626 140 7 days 120000 20000 10



Detections by Year:

Yr 2016 2017 2018 2019 2020
sites 19 31 19 32 26
detections 22 55 16 18 29
N.dot.model 10 13 8 7 16



WAIC

Models by WAIC:
model description WAIC N.total.est
fm7 counts 21.94854 127
fm6 lure + jdt + jdtSq 692.44325 58
fm5 jdt + jdtSq 710.85324 50
fm4 lure + jdt 731.56568 60
fm2 jdt 740.01517 51
fm1 dot 821.73284 54
fm3 lure 830.35690 57



Model summaries:



model: fm1
dot



summary table

param covariate iters_ef mean mode hdi_89pct_lower hdi_89pct_upper bayes_P pct.density
p[1] NA 4411 0.099 0.092 0.05 0.15 0 1.001
p[2] NA 8029 0.187 0.18 0.14 0.23 0 1.001
p[3] NA 4186 0.047 0.044 0.02 0.08 0 1.001
p[4] NA 7430 0.072 0.07 0.04 0.10 0 1.001
p[5] NA 4587 0.052 0.049 0.03 0.08 0 1.001
lambda[1] NA 3263 0.633 0.517 0.23 0.98 0 1.001
lambda[2] NA 9003 0.418 0.361 0.21 0.62 0 1.001
lambda[3] NA 3013 0.618 0.445 0.19 1.02 0 1.001
lambda[4] NA 8162 0.301 0.241 0.10 0.48 0 1.001
lambda[5] NA 4666 0.836 0.682 0.41 1.24 0 1.001
N[110] NA 7837 0.157 err 0.00 1.00 err err
N[78] NA 10000 0.119 err 0.00 1.00 err err
N[54] NA 5237 0.165 err 0.00 1.00 err err
N[40] NA 0 0.000 err 0.00 0.00 err err
N[83] NA 9488 1.072 err 1.00 1.00 err err

p[1]

p[2]

p[3]

p[4]

p[5]

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[110]

N[78]

N[54]

N[40]

## Warning in cor(X, use = "pairwise.complete.obs"): the standard deviation is zero
## Warning: Removed 50 rows containing missing values (geom_bar).

N[83]







model: fm2
jdt



summary table

param covariate iters_ef mean mode hdi_89pct_lower hdi_89pct_upper bayes_P pct.density
alpha jdt 5050 1.196 1.212 0.98 1.41 0 1.001
alpha0 NA 4191 -2.942 -2.935 -3.22 -2.67 0 1.001
lambda[1] NA 9140 0.576 0.518 0.27 0.87 0 1.001
lambda[2] NA 9246 0.653 0.598 0.33 0.97 0 1.001
lambda[3] NA 9712 0.467 0.409 0.19 0.72 0 1.001
lambda[4] NA 10000 0.258 0.22 0.10 0.41 0 1.001
lambda[5] NA 9521 0.724 0.67 0.40 1.02 0 1.001
N[85] NA 10000 1.028 err 1.00 1.00 err err
N[68] NA 9627 1.086 err 1.00 1.00 err err
N[32] NA 9295 1.422 1 1.00 2.00 0 1.0001
N[114] NA 10000 0.072 err 0.00 0.00 err err
N[40] NA 10000 0.016 err 0.00 0.00 err err

alpha

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[85]

N[68]

N[32]

N[114]

N[40]

alpha relationship







model: fm3
lure



summary table

param covariate iters_ef mean mode hdi_89pct_lower hdi_89pct_upper bayes_P pct.density
alpha lure 8065 -0.093 -0.074 -0.26 0.07 0.68622 0.8133
alpha0 NA 5904 -2.449 -2.441 -2.65 -2.23 0 1.001
lambda[1] NA 9196 0.648 0.554 0.30 0.99 0 1.001
lambda[2] NA 7422 0.749 0.67 0.40 1.07 0 1.001
lambda[3] NA 10000 0.411 0.326 0.16 0.63 0 1.001
lambda[4] NA 9650 0.271 0.234 0.11 0.42 0 1.001
lambda[5] NA 9422 0.652 0.603 0.37 0.94 0 1.001
N[56] NA 10000 0.020 err 0.00 0.00 err err
N[122] NA 10000 1.141 err 1.00 2.00 err err
N[54] NA 10000 0.020 err 0.00 0.00 err err
N[104] NA 9412 1.141 err 1.00 2.00 err err
N[14] NA 10000 0.090 err 0.00 0.00 err err

alpha

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[56]

N[122]

N[54]

N[104]

N[14]

alpha relationship







model: fm4
lure + jdt



summary table

param covariate iters_ef mean mode hdi_89pct_lower hdi_89pct_upper bayes_P pct.density
alpha[1] lureDays 3620 -0.602 -0.601 -0.83 -0.38 0 1.001
alpha[2] julianDt 3255 1.477 1.477 1.21 1.74 0 1.001
alpha0 NA 2557 -3.152 -3.129 -3.47 -2.83 0 1.001
lambda[1] NA 10000 0.475 0.421 0.21 0.72 0 1.001
lambda[2] NA 8912 0.523 0.457 0.25 0.78 0 1.001
lambda[3] NA 10000 0.444 0.375 0.16 0.67 0 1.001
lambda[4] NA 9170 0.272 0.24 0.10 0.44 0 1.001
lambda[5] NA 3270 1.353 1.173 0.62 2.02 0 1.001
N[88] NA 8054 0.002 err 0.00 0.00 err err
N[44] NA 10681 0.460 0 0.00 1.00 1 err
N[113] NA 6728 1.547 1 1.00 3.00 0 1.0001
N[77] NA 10000 0.103 err 0.00 0.00 err err
N[52] NA 10000 0.032 err 0.00 0.00 err err

alpha[1]

alpha[2]

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[88]

N[44]

N[113]

N[77]

N[52]







model: fm5
jdt + jdtSq



summary table

param covariate iters_ef mean mode hdi_89pct_lower hdi_89pct_upper bayes_P pct.density
alpha[1] julianDt 1954 -0.053 -0.054 -0.39 0.29 0.96348 0.6068
alpha[2] julianDtSq 1985 1.174 1.176 0.86 1.45 0 1.001
alpha0 NA 5409 -2.824 -2.822 -3.08 -2.56 0 1.001
lambda[1] NA 9106 0.661 0.572 0.31 1.01 0 1.001
lambda[2] NA 9606 0.466 0.402 0.22 0.70 0 1.001
lambda[3] NA 9465 0.499 0.418 0.20 0.78 0 1.001
lambda[4] NA 9419 0.237 0.212 0.09 0.38 0 1.001
lambda[5] NA 9358 0.734 0.653 0.41 1.05 0 1.001
N[39] NA 9669 0.017 err 0.00 0.00 err err
N[63] NA 10000 1.103 err 1.00 1.00 err err
N[93] NA 10000 0.000 err 0.00 0.00 err err
N[59] NA 9608 1.272 err 1.00 2.00 err err
N[77] NA 10000 0.001 err 0.00 0.00 err err

alpha[1]

alpha[2]

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[39]

N[63]

N[93]

N[59]

N[77]

julian date relationship







model: fm6
lure + jdt + jdtSq



summary table

param covariate iters_ef mean mode hdi_89pct_lower hdi_89pct_upper bayes_P pct.density
alpha[1] lureDays 3711 -0.636 -0.619 -0.83 -0.43 0 1.001
alpha[2] julianDt 2108 0.231 0.227 -0.13 0.57 0.6141 0.8493
alpha[3] julianDtSq 2326 1.216 1.23 0.92 1.53 0 1.001
alpha0 NA 3246 -3.123 -3.12 -3.44 -2.80 0 1.001
lambda[1] NA 9102 0.524 0.435 0.23 0.79 0 1.001
lambda[2] NA 10000 0.428 0.414 0.20 0.64 0 1.001
lambda[3] NA 8902 0.479 0.406 0.20 0.75 0 1.001
lambda[4] NA 9823 0.248 0.202 0.10 0.39 0 1.001
lambda[5] NA 3421 1.411 1.199 0.67 2.10 0 1.001
N[89] NA 10303 0.178 err 0.00 1.00 err err
N[100] NA 10000 0.000 err 0.00 0.00 err err
N[24] NA 10000 0.001 err 0.00 0.00 err err
N[93] NA 10000 0.000 err 0.00 0.00 err err
N[38] NA 10000 0.303 0 0.00 1.00 1 err

alpha[1]

alpha[2]

alpha[3]

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[89]

N[100]

N[24]

N[93]

N[38]







model: fm7
counts



summary table

param covariate iters_ef mean mode hdi_89pct_lower hdi_89pct_upper bayes_P pct.density
alpha counts 8541 16.781 16.592 14.61 18.87 0 1.001
alpha0 NA 8206 -5.162 -5.122 -5.96 -4.38 0 1.001
lambda[1] NA 9903 0.982 0.877 0.43 1.48 0 1.001
lambda[2] NA 8693 0.949 0.851 0.45 1.40 0 1.001
lambda[3] NA 9152 0.959 0.855 0.37 1.48 0 1.001
lambda[4] NA 6580 0.915 0.725 0.34 1.49 0 1.001
lambda[5] NA 10000 0.971 0.913 0.52 1.39 0 1.001
N[85] NA 7711 1.001 err 1.00 1.00 err err
N[62] NA 10576 1.047 err 1.00 1.00 err err
N[49] NA 9657 0.913 0 0.00 2.00 1 err
N[91] NA 9385 0.865 0 0.00 2.00 1 err
N[27] NA 0 1.000 err 1.00 1.00 err err

alpha

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[85]

N[62]

N[49]

N[91]

N[27]

## Warning in cor(X, use = "pairwise.complete.obs"): the standard deviation is zero
## Warning: Removed 50 rows containing missing values (geom_bar).

alpha relationship